Compressing image-to-image models with average smoothing

ABSTRACT

System and methods for compressing image-to-image models. Generative Adversarial Networks (GANs) have achieved success in generating high-fidelity images. An image compression system and method adds a novel variant to class-dependent parameters (CLADE), referred to as CLADE-Avg, which recovers the image quality without introducing extra computational cost. An extra layer of average smoothing is performed between the parameter and normalization layers. Compared to CLADE, this image compression system and method smooths abrupt boundaries, and introduces more possible values for the scaling and shift. In addition, the kernel size for the average smoothing can be selected as a hyperparameter, such as a 3×3 kernel size. This method does not introduce extra multiplications but only addition, and thus does not introduce much computational overhead, as the division can be absorbed into the parameters after training.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. application Ser. No.17/191,970 filed on Mar. 4, 2021, the contents of which are incorporatedfully herein by reference.

TECHNICAL FIELD

Examples set forth herein generally relate to a generative adversarialnetwork (GAN). The example include, but are not limited to, methods andsystems for compressing image-to-image models.

BACKGROUND

A GAN is a machine learning framework in which two neural networks, adiscriminator network and a generator network, contest with each otherin a zero-sum game. Given a training data set, a GAN trained modellearns to generate new data with the same statistics as the trainingset.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some nonlimiting examples areillustrated in the figures of the accompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples;

FIG. 2 is a diagrammatic representation of a messaging system, inaccordance with some examples, that has both client-side and server-sidefunctionality;

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples;

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples;

FIG. 5 is a flowchart for an access-limiting process, in accordance withsome examples;

FIG. 6 is an illustration of a generative adversarial networkarchitecture, according to some examples;

FIG. 7 is an illustration of a residual block, according to someexamples;

FIG. 8A illustrates a method in accordance with one example;

FIG. 8B illustrates introducing spatial-dependency (SPADE) method oninput to the learned scaling and shift parameters of the normalizationlayers;

FIG. 8C illustrates a class adaptive (CLADE) method using an inputclass, instead of input pixel information, to determine the scaling andshifted parameters in the following normalization layer;

FIG. 8D illustrates a CLADE-Avg method where an extra layer of averagesmoothing is provided between the parameter and normalization layer thatsmooths the abrupt boundaries, and introduces more possible values forthe scaling and shift;

FIG. 8E illustrates an example method for generating a compressedimage-to-image model using Clade-Avg method;

FIG. 9 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples; and

FIG. 10 is a block diagram showing a software architecture within whichexamples may be implemented.

DETAILED DESCRIPTION

This disclosure includes methods and a system for compressingimage-to-image models. Generative Adversarial Networks (GANs) haveachieved success in generating high-fidelity images. Examples set forthin this disclosure describe an image compression system and method thatadds a novel variant to CLADE parameters, referred to in this disclosureas CLADE-Avg, which recovers the image quality without introducing extracomputational cost. This disclosure introduces an extra layer of averagesmoothing between the parameter and normalization layers. Compared toCLADE, this image compression system and method smooths abruptboundaries, and introduces more possible values for the scaling andshift. In addition, the kernel size for the average smoothing can beselected as a hyperparameter, such as a 3×3 kernel size. This methoddoes not introduce extra multiplications but only addition, and thusdoes not introduce much computational overhead, as the division can beabsorbed into the parameters after training.

The image compression system includes inception-based residual blocksinto the generator network of the teacher network (e.g., a first GAN).The image compression system uses a one-step pruning method to searchfor a student network (e.g., a second GAN) from the teacher network. Theimage compression system trains the student network using knowledgedistillation by maximizing feature similarity between the teachernetwork and the student network using a similarity metric. The resultingtrained student network contains less parameters than the teachernetwork and are more computationally efficient than its teacher networkcounterpart. The trained student network may run on a mobile computingdevice with lower computation costs than the teacher network.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104 and other applications 106. Each messaging client 104 iscommunicatively coupled to other instances of the messaging client 104(e.g., hosted on respective other client devices 102), a messagingserver system 108 and third-party servers 110 via a network 112 (e.g.,the Internet). A messaging client 104 can also communicate withlocally-hosted applications 106 using Applications Program Interfaces(APIs).

A messaging client 104 is able to communicate and exchange data withother messaging clients 104 and with the messaging server system 108 viathe network 112. The data exchanged between messaging clients 104, andbetween a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 112 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces (UIs) of the messaging client104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 116 is coupled to, andprovides a programmatic interface to, application servers 114. Theapplication servers 114 are communicatively coupled to a database server120, which facilitates access to a database 126 that stores dataassociated with messages processed by the application servers 114.Similarly, a web server 128 is coupled to the application servers 114,and provides web-based interfaces to the application servers 114. Tothis end, the web server 128 processes incoming network requests overthe Hypertext Transfer Protocol (HTTP) and several other relatedprotocols.

The Application Program Interface (API) server 116 receives andtransmits message data (e.g., commands and message payloads) between theclient device 102 and the application servers 114. Specifically, theApplication Program Interface (API) server 116 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 114. The Application Program Interface (API) server116 exposes various functions supported by the application servers 114,including account registration, login functionality, the sending ofmessages, via the application servers 114, from a particular messagingclient 104 to another messaging client 104, the sending of media files(e.g., images or video) from a messaging client 104 to a messagingserver 118, and for possible access by another messaging client 104, thesettings of a collection of media data (e.g., story), the retrieval of alist of friends of a user of a client device 102, the retrieval of suchcollections, the retrieval of messages and content, the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph), the location of friends within a social graph, and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 114 host a number of server applications andsubsystems, including for example a messaging server 118, an imageprocessing server 122, a social network server 124 and an imagecompression system 130. The messaging server 118 implements a number ofmessage processing technologies and functions, particularly related tothe aggregation and other processing of content (e.g., textual andmultimedia content) included in messages received from multipleinstances of the messaging client 104. As will be described in furtherdetail, the text and media content from multiple sources may beaggregated into collections of content (e.g., called stories orgalleries). These collections are then made available to the messagingclient 104. Other processor and memory intensive processing of data mayalso be performed server-side by the messaging server 118, in view ofthe hardware requirements for such processing.

The application servers 114 also include an image processing server 122that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 118.

The social network server 124 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 118. To this end, the social network server 124maintains and accesses an entity graph 308 (as shown in FIG. 3 ) withinthe database 126. Examples of functions and services supported by thesocial network server 124 include the identification of other users ofthe messaging system 100 with which a particular user has relationshipsor is “following,” and also the identification of other entities andinterests of a particular user.

The image compression system 130 searches a teacher network to generatea computationally efficient, student network. The image compressionsystem 130 leverages a residual block that is specially configured togenerate a search space from which an efficient student network may befound.

Returning to the messaging client 104, features and functions of anexternal resource (e.g., an application 106 or applet) are madeavailable to a user via an interface of the messaging client 104. Inthis context, “external” refers to the fact that the application 106 orapplet is external to the messaging client 104. The external resource isoften provided by a third party but may also be provided by the providerof the messaging client 104. The messaging client 104 receives a userselection of an option to launch or access features of such an externalresource. The external resource may be the application 106 installed onthe client device 102 (e.g., a “native app”), or a small-scale versionof the application (e.g., an “applet”) that is hosted on the clientdevice 102 or remote of the client device 102 (e.g., on third-partyservers 110). The small-scale version of the application includes asubset of features and functions of the application (e.g., thefull-scale, native version of the application) and is implemented usinga markup-language document. In one example, the small-scale version ofthe application (e.g., an “applet”) is a web-based, markup-languageversion of the application and is embedded in the messaging client 104.In addition to using markup-language documents (e.g., a .*ml file), anapplet may incorporate a scripting language (e.g., a .*js file or a.json file) and a style sheet (e.g., a .*ss file).

In response to receiving a user selection of the option to launch oraccess features of the external resource, the messaging client 104determines whether the selected external resource is a web-basedexternal resource or a locally-installed application 106. In some cases,applications 106 that are locally installed on the client device 102 canbe launched independently of and separately from the messaging client104, such as by selecting an icon, corresponding to the application 106,on a home screen of the client device 102. Small-scale versions of suchapplications can be launched or accessed via the messaging client 104and, in some examples, no or limited portions of the small-scaleapplication can be accessed outside of the messaging client 104. Thesmall-scale application can be launched by the messaging client 104receiving, from a third-party server 110 for example, a markup-languagedocument associated with the small-scale application and processing sucha document.

In response to determining that the external resource is alocally-installed application 106, the messaging client 104 instructsthe client device 102 to launch the external resource by executinglocally-stored code corresponding to the external resource. In responseto determining that the external resource is a web-based resource, themessaging client 104 communicates with the third-party servers 110 (forexample) to obtain a markup-language document corresponding to theselected external resource. The messaging client 104 then processes theobtained markup-language document to present the web-based externalresource within a user interface of the messaging client 104.

The messaging client 104 can notify a user of the client device 102, orother users related to such a user (e.g., “friends”), of activity takingplace in one or more external resources. For example, the messagingclient 104 can provide participants in a conversation (e.g., a chatsession) in the messaging client 104 with notifications relating to thecurrent or recent use of an external resource by one or more members ofa group of users. One or more users can be invited to join in an activeexternal resource or to launch a recently-used but currently inactive(in the group of friends) external resource. The external resource canprovide participants in a conversation, each using respective messagingclients 104, with the ability to share an item, status, state, orlocation in an external resource with one or more members of a group ofusers into a chat session. The shared item may be an interactive chatcard with which members of the chat can interact, for example, to launchthe corresponding external resource, view specific information withinthe external resource, or take the member of the chat to a specificlocation or state within the external resource. Within a given externalresource, response messages can be sent to users on the messaging client104. The external resource can selectively include different media itemsin the responses, based on a current context of the external resource.

The messaging client 104 can present a list of the available externalresources (e.g., applications 106 or applets) to a user to launch oraccess a given external resource. This list can be presented in acontext-sensitive menu. For example, the icons representing differentones of the application 106 (or applets) can vary based on how the menuis launched by the user (e.g., from a conversation interface or from anon-conversation interface).

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 114. The messaging system 100 embodies a numberof subsystems, which are supported on the client-side by the messagingclient 104 and on the sever-side by the application servers 114. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, an augmentation system 208, a mapsystem 210, a game system 212, an external resource system 214, and animage compression system 130.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 118. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 furthermore includes a curationinterface 206 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface206 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 208 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system208 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 208 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 208 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text or image that can be overlaid on top of aphotograph taken by the client device 102. In another example, the mediaoverlay includes an identification of a location overlay (e.g., Venicebeach), a name of a live event, or a name of a merchant overlay (e.g.,Beach Coffee House). In another example, the augmentation system 208uses the geolocation of the client device 102 to identify a mediaoverlay that includes the name of a merchant at the geolocation of theclient device 102. The media overlay may include other indiciaassociated with the merchant. The media overlays may be stored in thedatabase 126 and accessed through the database server 120.

In some examples, the augmentation system 208 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 208 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

In other examples, the augmentation system 208 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the augmentation system 208 associates the media overlay of thehighest bidding merchant with a corresponding geolocation for apredefined amount of time.

The map system 210 provides various geographic location functions, andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 210 enables thedisplay of user icons or avatars (e.g., stored in profile data 316) on amap to indicate a current or past location of “friends” of a user, aswell as media content (e.g., collections of messages includingphotographs and videos) generated by such friends, within the context ofa map. For example, a message posted by a user to the messaging system100 from a specific geographic location may be displayed within thecontext of a map at that particular location to “friends” of a specificuser on a map interface of the messaging client 104. A user canfurthermore share his or her location and status information (e.g.,using an appropriate status avatar) with other users of the messagingsystem 100 via the messaging client 104, with this location and statusinformation being similarly displayed within the context of a mapinterface of the messaging client 104 to selected users.

The game system 212 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games that can be launched by auser within the context of the messaging client 104, and played withother users of the messaging system 100. The messaging system 100further enables a particular user to invite other users to participatein the play of a specific game, by issuing invitations to such otherusers from the messaging client 104. The messaging client 104 alsosupports both the voice and text messaging (e.g., chats) within thecontext of gameplay, provides a leaderboard for the games, and alsosupports the provision of in-game rewards (e.g., coins and items).

The external resource system 214 provides an interface for the messagingclient 104 to communicate with remote servers (e.g., third-party servers110) to launch or access external resources, i.e., applications orapplets. Each third-party server 110 hosts, for example, a markuplanguage (e.g., HTML5) based application or small-scale version of anapplication (e.g., game, utility, payment, or ride-sharing application).The messaging client 104 may launches a web-based resource (e.g.,application) by accessing the HTML5 file from the third-party servers110 associated with the web-based resource. In certain examples,applications hosted by third-party servers 110 are programmed inJavaScript leveraging a Software Development Kit (SDK) provided by themessaging server 118. The SDK includes Application ProgrammingInterfaces (APIs) with functions that can be called or invoked by theweb-based application. In certain examples, the messaging server 118includes a JavaScript library that provides a given external resourceaccess to certain user data of the messaging client 104. HTML5 is usedas an example technology for programming games, but applications andresources programmed based on other technologies can be used.

In order to integrate the functions of the SDK into the web-basedresource, the SDK is downloaded by a third-party server 110 from themessaging server 118 or is otherwise received by the third-party server110. Once downloaded or received, the SDK is included as part of theapplication code of a web-based external resource. The code of theweb-based resource can then call or invoke certain functions of the SDKto integrate features of the messaging client 104 into the web-basedresource.

The SDK stored on the messaging server 118 effectively provides thebridge between an external resource (e.g., applications 106 or appletsand the messaging client 104. This provides the user with a seamlessexperience of communicating with other users on the messaging client104, while also preserving the look and feel of the messaging client104. To bridge communications between an external resource and amessaging client 104, in certain examples, the SDK facilitatescommunication between third-party servers 110 and the messaging client104. In certain examples, a Web ViewJavaScriptBridge running on a clientdevice 102 establishes two one-way communication channels between anexternal resource and the messaging client 104. Messages are sentbetween the external resource and the messaging client 104 via thesecommunication channels asynchronously. Each SDK function invocation issent as a message and callback. Each SDK function is implemented byconstructing a unique callback identifier and sending a message withthat callback identifier.

By using the SDK, not all information from the messaging client 104 isshared with third-party servers 110. The SDK limits which information isshared based on the needs of the external resource. In certain examples,each third-party server 110 provides an HTML5 file corresponding to theweb-based external resource to the messaging server 118. The messagingserver 118 can add a visual representation (such as a box art or othergraphic) of the web-based external resource in the messaging client 104.Once the user selects the visual representation or instructs themessaging client 104 through a GUI of the messaging client 104 to accessfeatures of the web-based external resource, the messaging client 104obtains the HTML5 file and instantiates the resources necessary toaccess the features of the web-based external resource.

The messaging client 104 presents a graphical user interface (e.g., alanding page or title screen) for an external resource. During, before,or after presenting the landing page or title screen, the messagingclient 104 determines whether the launched external resource has beenpreviously authorized to access user data of the messaging client 104.In response to determining that the launched external resource has beenpreviously authorized to access user data of the messaging client 104,the messaging client 104 presents another graphical user interface ofthe external resource that includes functions and features of theexternal resource. In response to determining that the launched externalresource has not been previously authorized to access user data of themessaging client 104, after a threshold period of time (e.g., 3 seconds)of displaying the landing page or title screen of the external resource,the messaging client 104 slides up (e.g., animates a menu as surfacingfrom a bottom of the screen to a middle of or other portion of thescreen) a menu for authorizing the external resource to access the userdata. The menu identifies the type of user data that the externalresource will be authorized to use. In response to receiving a userselection of an accept option, the messaging client 104 adds theexternal resource to a list of authorized external resources and allowsthe external resource to access user data from the messaging client 104.In some examples, the external resource is authorized by the messagingclient 104 to access the user data in accordance with an OAuth 2framework.

The messaging client 104 controls the type of user data that is sharedwith external resources based on the type of external resource beingauthorized. For example, external resources that include full-scaleapplications (e.g., an application 106) are provided with access to afirst type of user data (e.g., only two-dimensional avatars of userswith or without different avatar characteristics). As another example,external resources that include small-scale versions of applications(e.g., web-based versions of applications) are provided with access to asecond type of user data (e.g., payment information, two-dimensionalavatars of users, three-dimensional avatars of users, and avatars withvarious avatar characteristics). Avatar characteristics includedifferent ways to customize a look and feel of an avatar, such asdifferent poses, facial features, clothing, and so forth.

The image compression system 130 searches a teacher network to generatea computationally efficient, student network. The image compressionsystem 130 leverages a residual block that is specially configured togenerate a search space from which an efficient student network may befound. Aspects of the image compression system 130 may exist on themessaging client 104 and other aspects may exist on the applicationservers 114. In some examples, the image compression system 130 operatesexclusively on the messaging client 104.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 126 of the messaging server system 108,according to certain examples. While the content of the database 126 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 126 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload. Further details regarding information that may be included in amessage, and included within the message data stored in the messagetable 302 is described below with reference to FIG. 4 .

An entity table 306 stores entity data, and is linked (e.g.,referentially) to an entity graph 308 and profile data 316. Entities forwhich records are maintained within the entity table 306 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 308 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization)interested-based or activity-based, merely for example.

The profile data 316 stores multiple types of profile data about aparticular entity. The profile data 316 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 316 includes, for example, a user name,telephone number, address, settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100, andon map interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

Where the entity is a group, the profile data 316 for the group maysimilarly include one or more avatar representations associated with thegroup, in addition to the group name, members, and various settings(e.g., notifications) for the relevant group.

The database 126 also stores augmentation data, such as overlays orfilters, in an augmentation table 310. The augmentation data isassociated with and applied to videos (for which data is stored in avideo table 304) and images (for which data is stored in an image table312).

Filters, in one example, are overlays that are displayed as overlaid onan image or video during presentation to a recipient user. Filters maybe of various types, including user-selected filters from a set offilters presented to a sending user by the messaging client 104 when thesending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 102.

Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client 104, based on otherinputs or information gathered by the client device 102 during themessage generation process. Examples of data filters include currenttemperature at a specific location, a current speed at which a sendinguser is traveling, battery life for a client device 102, or the currenttime.

Other augmentation data that may be stored within the image table 312includes augmented reality content items (e.g., corresponding toapplying Lenses or augmented reality experiences). An augmented realitycontent item may be a real-time special effect and sound that may beadded to an image or a video.

As described above, augmentation data includes augmented reality contentitems, overlays, image transformations, AR images, and similar termsrefer to modifications that may be applied to image data (e.g., videosor images). This includes real-time modifications, which modify an imageas it is captured using device sensors (e.g., one or multiple cameras)of a client device 102 and then displayed on a screen of the clientdevice 102 with the modifications. This also includes modifications tostored content, such as video clips in a gallery that may be modified.For example, in a client device 102 with access to multiple augmentedreality content items, a user can use a single video clip with multipleaugmented reality content items to see how the different augmentedreality content items will modify the stored clip. For example, multipleaugmented reality content items that apply different pseudorandommovement models can be applied to the same content by selectingdifferent augmented reality content items for the content. Similarly,real-time video capture may be used with an illustrated modification toshow how video images currently being captured by sensors of a clientdevice 102 would modify the captured data. Such data may simply bedisplayed on the screen and not stored in memory, or the contentcaptured by the device sensors may be recorded and stored in memory withor without the modifications (or both). In some systems, a previewfeature can show how different augmented reality content items will lookwithin different windows in a display at the same time. This can, forexample, enable multiple windows with different pseudorandom animationsto be viewed on a display at the same time.

Data and various systems using augmented reality content items or othersuch transform systems to modify content using this data can thusinvolve detection of objects (e.g., faces, hands, bodies, cats, dogs,surfaces, objects, etc.), tracking of such objects as they leave, enter,and move around the field of view in video frames, and the modificationor transformation of such objects as they are tracked. In variousexamples, different methods for achieving such transformations may beused. Some examples may involve generating a three-dimensional meshmodel of the object or objects, and using transformations and animatedtextures of the model within the video to achieve the transformation. Inother examples, tracking of points on an object may be used to place animage or texture (which may be two dimensional or three dimensional) atthe tracked position. In still further examples, neural network analysisof video frames may be used to place images, models, or textures incontent (e.g., images or frames of video). Augmented reality contentitems thus refer both to the images, models, and textures used toproduce transformations in content, as well as to additional modelingand analysis information needed to achieve such transformations withobject detection, tracking, and placement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device, or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human's face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device, and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of object's elements characteristic points for each element of anobject are calculated (e.g., using an Active Shape Model (ASM) or otherknown methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshused in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A first set of first pointsis generated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh, and thenvarious areas based on the points are generated. The elements of theobject are then tracked by aligning the area for each element with aposition for each of the at least one element, and properties of theareas can be modified based on the request for modification, thustransforming the frames of the video stream. Depending on the specificrequest for modification properties of the mentioned areas can betransformed in different ways. Such modifications may involve changingcolor of areas; removing at least some part of areas from the frames ofthe video stream; including one or more new objects into areas which arebased on a request for modification; and modifying or distorting theelements of an area or object. In various examples, any combination ofsuch modifications or other similar modifications may be used. Forcertain models to be animated, some characteristic points can beselected as control points to be used in determining the entirestate-space of options for the model animation.

In some examples of a computer animation model to transform image datausing face detection, the face is detected on an image with use of aspecific face detection algorithm (e.g., Viola-Jones). Then, an ActiveShape Model (ASM) algorithm is applied to the face region of an image todetect facial feature reference points.

Other methods and algorithms suitable for face detection can be used.For example, in some examples, features are located using a landmark,which represents a distinguishable point present in most of the imagesunder consideration. For facial landmarks, for example, the location ofthe left eye pupil may be used. If an initial landmark is notidentifiable (e.g., if a person has an eyepatch), secondary landmarksmay be used. Such landmark identification procedures may be used for anysuch objects. In some examples, a set of landmarks forms a shape. Shapescan be represented as vectors using the coordinates of the points in theshape. One shape is aligned to another with a similarity transform(allowing translation, scaling, and rotation) that minimizes the averageEuclidean distance between shape points. The mean shape is the mean ofthe aligned training shapes.

In some examples, a search for landmarks from the mean shape aligned tothe position and size of the face determined by a global face detectoris started. Such a search then repeats the steps of suggesting atentative shape by adjusting the locations of shape points by templatematching of the image texture around each point and then conforming thetentative shape to a global shape model until convergence occurs. Insome systems, individual template matches are unreliable, and the shapemodel pools the results of the weak template matches to form a strongeroverall classifier. The entire search is repeated at each level in animage pyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a clientdevice (e.g., the client device 102) and perform complex imagemanipulations locally on the client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, and any othersuitable image or video manipulation implemented by a convolutionalneural network that has been configured to execute efficiently on theclient device 102.

In some examples, a computer animation model to transform image data canbe used by a system where a user may capture an image or video stream ofthe user (e.g., a selfie) using a client device 102 having a neuralnetwork operating as part of a messaging client 104 operating on theclient device 102. The transformation system operating within themessaging client 104 determines the presence of a face within the imageor video stream and provides modification icons associated with acomputer animation model to transform image data, or the computeranimation model can be present as associated with an interface describedherein. The modification icons include changes that may be the basis formodifying the user's face within the image or video stream as part ofthe modification operation. Once a modification icon is selected, thetransform system initiates a process to convert the image of the user toreflect the selected modification icon (e.g., generate a smiling face onthe user). A modified image or video stream may be presented in agraphical user interface displayed on the client device 102 as soon asthe image or video stream is captured, and a specified modification isselected. The transformation system may implement a complexconvolutional neural network on a portion of the image or video streamto generate and apply the selected modification. That is, the user maycapture the image or video stream and be presented with a modifiedresult in real-time or near real-time once a modification icon has beenselected. Further, the modification may be persistent while the videostream is being captured, and the selected modification icon remainstoggled. Machine taught neural networks may be used to enable suchmodifications.

The graphical user interface, presenting the modification performed bythe transform system, may supply the user with additional interactionoptions. Such options may be based on the interface used to initiate thecontent capture and selection of a particular computer animation model(e.g., initiation from a content generation user interface). In variousexamples, a modification may be persistent after an initial selection ofa modification icon. The user may toggle the modification on or off bytapping or otherwise selecting the face being modified by thetransformation system and store it for later viewing or browse to otherareas of the imaging application. Where multiple faces are modified bythe transformation system, the user may toggle the modification on oroff globally by tapping or selecting a single face modified anddisplayed within a graphical user interface. In some examples,individual faces, among a group of multiple faces, may be individuallymodified, or such modifications may be individually toggled by tappingor selecting the individual face or a series of individual facesdisplayed within the graphical user interface.

A story table 314 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The generation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 306). A user maygenerate a “personal story” in the form of a collection of content thathas been generated and sent/broadcast by that user. To this end, theuser interface of the messaging client 104 may include an icon that isuser-selectable to enable a sending user to add specific content to hisor her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is generated manually,automatically, or using a combination of manual and automatictechniques. For example, a “live story” may constitute a curated streamof user-submitted content from varies locations and events. Users whoseclient devices have location services enabled and are at a commonlocation event at a particular time may, for example, be presented withan option, via a user interface of the messaging client 104, tocontribute content to a particular live story. The live story may beidentified to the user by the messaging client 104, based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some examples, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

As mentioned above, the video table 304 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 312 storesimage data associated with messages for which message data is stored inthe entity table 306. The entity table 306 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 312 and the video table 304.

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server118. The content of a particular message 400 is used to populate themessage table 302 stored within the database 126, accessible by themessaging server 118. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 114. A message 400 is shown to include thefollowing example components:

-   -   message identifier 402: a unique identifier that identifies the        message 400.    -   message text payload 404: text, to be generated by a user via a        user interface of the client device 102, and that is included in        the message 400.    -   message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from a memory        component of a client device 102, and that is included in the        message 400. Image data for a sent or received message 400 may        be stored in the image table 312.    -   message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400. Video data        for a sent or received message 400 may be stored in the video        table 304.    -   message audio payload 410: audio data, captured by a microphone        or retrieved from a memory component of the client device 102,        and that is included in the message 400.    -   message augmentation data 412: augmentation data (e.g., filters,        stickers, or other annotations or enhancements) that represents        augmentations to be applied to message image payload 406,        message video payload 408, or message audio payload 410 of the        message 400. Augmentation data for a sent or received message        400 may be stored in the augmentation table 310.    -   message duration parameter 414: parameter value indicating, in        seconds, the amount of time for which content of the message        (e.g., the message image payload 406, message video payload 408,        message audio payload 410) is to be presented or made accessible        to a user via the messaging client 104.    -   message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respect to content        items included in the content (e.g., a specific image into        within the message image payload 406, or a specific video in the        message video payload 408).    -   message story identifier 418: identifier values identifying one        or more content collections (e.g., “stories” identified in the        story table 314) with which a particular content item in the        message image payload 406 of the message 400 is associated. For        example, multiple images within the message image payload 406        may each be associated with multiple content collections using        identifier values.    -   message tag 420: each message 400 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 406        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 420 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   message sender identifier 422: an identifier (e.g., a messaging        system identifier, email address, or device identifier)        indicative of a user of the Client device 102 on which the        message 400 was generated and from which the message 400 was        sent.    -   message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 312.Similarly, values within the message video payload 408 may point to datastored within a video table 304, values stored within the messageaugmentations 412 may point to data stored in an augmentation table 310,values stored within the message story identifier 418 may point to datastored in a story table 314, and values stored within the message senderidentifier 422 and the message receiver identifier 424 may point to userrecords stored within an entity table 306.

Time-Based Access Limitation Architecture

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message group 504) may be time-limited (e.g., madeephemeral).

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client 104. In one example,an ephemeral message 502 is viewable by a receiving user for up to amaximum of 10 seconds, depending on the amount of time that the sendinguser specifies using the message duration parameter 506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 510, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 510 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message group 504 (e.g., a collection of messages in apersonal story, or an event story). The ephemeral message group 504 hasan associated group duration parameter 508, a value of which determinesa time duration for which the ephemeral message group 504 is presentedand accessible to users of the messaging system 100. The group durationparameter 508, for example, may be the duration of a music concert,where the ephemeral message group 504 is a collection of contentpertaining to that concert. Alternatively, a user (either the owninguser or a curator user) may specify the value for the group durationparameter 508 when performing the setup and generation of the ephemeralmessage group 504.

Additionally, each ephemeral message 502 within the ephemeral messagegroup 504 has an associated group participation parameter 512, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message group504. Accordingly, a particular ephemeral message group 504 may “expire”and become inaccessible within the context of the ephemeral messagegroup 504, prior to the ephemeral message group 504 itself expiring interms of the group duration parameter 508. The group duration parameter508, group participation parameter 512, and message receiver identifier424 each provide input to a group timer 514, which operationallydetermines, firstly, whether a particular ephemeral message 502 of theephemeral message group 504 will be displayed to a particular receivinguser and, if so, for how long. Note that the ephemeral message group 504is also aware of the identity of the particular receiving user as aresult of the message receiver identifier 424.

Accordingly, the group timer 514 operationally controls the overalllifespan of an associated ephemeral message group 504, as well as anindividual ephemeral message 502 included in the ephemeral message group504. In one example, each and every ephemeral message 502 within theephemeral message group 504 remains viewable and accessible for a timeperiod specified by the group duration parameter 508. In a furtherexample, a certain ephemeral message 502 may expire, within the contextof ephemeral message group 504, based on a group participation parameter512. Note that a message duration parameter 506 may still determine theduration of time for which a particular ephemeral message 502 isdisplayed to a receiving user, even within the context of the ephemeralmessage group 504. Accordingly, the message duration parameter 506determines the duration of time that a particular ephemeral message 502is displayed to a receiving user, regardless of whether the receivinguser is viewing that ephemeral message 502 inside or outside the contextof an ephemeral message group 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message group 504based on a determination that it has exceeded an associated groupparticipation parameter 512. For example, when a sending user hasestablished a group participation parameter 512 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message group 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message group 504 when either the groupparticipation parameter 512 for each and every ephemeral message 502within the ephemeral message group 504 has expired, or when theephemeral message group 504 itself has expired in terms of the groupduration parameter 508.

In certain use cases, a generator of a particular ephemeral messagegroup 504 may specify an indefinite group duration parameter 508. Inthis case, the expiration of the group participation parameter 512 forthe last remaining ephemeral message 502 within the ephemeral messagegroup 504 will determine when the ephemeral message group 504 itselfexpires. In this case, a new ephemeral message 502, added to theephemeral message group 504, with a new group participation parameter512, effectively extends the life of an ephemeral message group 504 toequal the value of the group participation parameter 512.

Responsive to the ephemeral timer system 202 determining that anephemeral message group 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (and, for example, specifically the messaging client 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage group 504 to no longer be displayed within a user interface ofthe messaging client 104. Similarly, when the ephemeral timer system 202determines that the message duration parameter 506 for a particularephemeral message 502 has expired, the ephemeral timer system 202 causesthe messaging client 104 to no longer display an indicium (e.g., an iconor textual identification) associated with the ephemeral message 502.

Generative Adversarial Networks

FIG. 6 is an illustration of a GAN architecture, according to someexamples. In some examples, the image compression system 130 comprisesof a GAN. The generator 604 and the discriminator 610 are neuralnetworks. Each network can be any neural network such as an artificialneural network, a convolutional neural network, a recurrent neuralnetwork, etc. The output of the generator 604 is linked directly theinput of the discriminator 610. Using backpropagation, thediscriminator's classification provides a signal that the generator usesto update its weights.

The discriminator 610 is a classifier that tries to distinguish realdata from artificial data (e.g., data generated by the generator). Thediscriminator 610 is trained using two data sources: real data 606 andfake data 608. The real data 606 may comprise real human faces and thefake data 608 may comprise artificial human faces. The fake data 608 isdata that is generated by the generator 604. During the training of thediscriminator 610, the discriminator 610 classifies the real data 606and the fake data 608. The discriminator loss 612 accommodate for thediscriminator 610 misclassifying real data 606 as fake and fake data 608as real. The discriminator 610 updates its weights (e.g., weights of theneural network layers) through backpropagation using the discriminatorloss 612.

The generator 604 is a neural network that generates fake data 608 basedon feedback from the discriminator. It learns to make the discriminator610 classify the fake data 608 as real. The generator 604 takes randomnoise 616 as input and transforms the random noise 616 into meaningfuloutput data. The generator loss accommodates for the generator 604producing fake data 608 that the discriminator 610 classifies as fake.The generator 604 updates its weights through backpropagation throughthe discriminator 610 and the generator 604 using the generator loss614.

In some examples the image compression system 130 uses a pre-trained GAN(e.g., Pix2Pix, CYCLEGAN, GauGAN).

FIG. 7 is an illustration of a residual block 702 used by the imagecompression system 130, according to some examples. The residual block702 is an inception-based residual block. A conventional residual blockin an image generator network (e.g., generator 604) only containsconvolution layers with one kernel size. However, the inception-basedresidual block 702 contains convolutional layers with different kernelsizes (e.g., 1×1, 3×3, and 5×5). The inception-based residual block 702incorporates depth-wise blocks (blocks 704, 706, 708) (e.g., depth-wiseconvolutional layers). Depth-wise convolutional layers require lesscomputation cost without sacrificing performance and are suitable forneural networks that are deployed on mobile computing devices. Theinception-based residual block 702 includes six types of operations(e.g., with two types of convolution layers and three differentkernel-sizes). Normalization layers (e.g., BN) and ReLU are appliedbetween each two consecutive convolution layers. In some examples, anormalization layer is inserted after summing features from the sixblocks and the residual connection.

The number of output channels for the first convolution layers of eachoperation is set to that of the original residual blocks divided by six(e.g., the number of different operations in the residual block 702). Insome examples, all residual blocks in the GAN are replaced with theresidual block 702.

FIG. 8A is an example method for generating a compressed image-to-imagemodel, according to examples. The method 800 can be performed by theimage compression system 130 in FIG. 1 . In one example, a processor (orcircuitry dedicated to performing instructed tasks) included in theimage compression system 130 performs the method 800 or causes the imagecomponent to perform the method 800.

Although the described flowcharts can show operations as a sequentialprocess, many of the operations can be performed in parallel orconcurrently. In addition, the order of the operations may bere-arranged. A process is terminated when its operations are completed.A process may correspond to a method, a procedure, an algorithm, etc.The operations of methods may be performed in whole or in part, may beperformed in conjunction with some or all of the operations in othermethods, and may be performed by any number of different systems, suchas the systems described herein, or any portion thereof, such as aprocessor included in any of the systems.

In operation 802, the image compression system 130 generates a firstgenerative adversarial network (GAN) comprising a first type ofconvolutional layer, a second type of convolutional layer and aplurality of kernel sizes. For example, the first type of convolutionallayer may be a conventional convolutional layer and the second type ofconvolutional layer may be a depth-wise convolutional layer. Theplurality of kernel sizes may be, for example, 1×1, 3×3, and 5×5. Inoperation 804, the image compression system 130 identifies a threshold.The image compression system 130 determines a scale threshold by abinary search on the scaling factors of normalization layers from thepre-trained teacher network. For example, the image compression system130 may temporarily prune all channels with a scaling factor magnitudesmaller than the threshold and measure the computational cost of thepruned model.

If the cost is smaller than the computational budget, the model ispruned too much and the image compression system 130 searches in a lowerinterval to get a smaller threshold. Otherwise, the image compressionsystem 130 searches in an upper interval to get a larger value. Duringthis process, the image compression system 130 retains the number ofoutput channels for convolution layers outside the residual block 702larger than a predefined value to avoid an invalid model. The thresholdrepresenting a measure of computational costs of the GAN. In someexamples, the threshold is a number of multiply-accumulate operations(MACs). In some examples, the threshold is a measure of computationallatency. It is to be understood that any measure of computational costmay be used as the threshold.

In operation 806, based on the threshold, the image compression system130 generates a second GAN by pruning channels of the first GAN. In someexamples, the image compression system 130 prunes the channels throughthe magnitudes of scaling factors in normalization layers, such as BatchNormalization (BN) and Instance Normalization (IN). All channels withscale smaller than the threshold are pruned until the final modelachieves the target computation budget. Given a residual block 702, theimage compression system 130 may change both the number of channels ineach layer and modify the operation, such that, e.g., one residual block702 may include layers with kernel sizes 1×1 and 3×3. The imagecompression system 130 prunes channels of the normalization layerstogether with the corresponding convolution layers. Specifically, theimage compression system 130 prunes the first normalization layers foreach operation in the residual block 702, namely the ones after thefirst k×k convolution layers for conventional operations and the onesafter the first 1×1 convolution layers for depth-wise operations.

An example algorithm for searching via the one-step pruning describedabove is as follows,

Algorithm 1 Searching via One-Step Pruning. Require: Computationalbudget T_(b), teacher model G_(T), scaling  factors γ_(i) ^((l)) (usedfor pruning) of the i-th channel in normalization  layers N^((l))∈G_(T),minimum # out-put channels c_(lb) for convolution  layers (outside theIn-cResBlock). Ensure: pruned student architecture G_(S).  1:$\left. {{Initialize}{scale}{lower}{bound}\gamma_{lo}:\gamma_{lo}}\leftarrow{\min\limits_{i,l}{{❘\gamma_{i}^{(l)}❘}.}} \right.$ 2:$\left. {{Initialize}{scale}{upper}{bound}\gamma_{hi}:\gamma_{hi}}\leftarrow{\max\limits_{i,l}{{❘\gamma_{i}^{(l)}❘}.}} \right.$ 3: while γ_(lo) < γ_(hi) do  4:  γ_(th) ← (γ_(lo) + γ_(hi))/2  5: Prune channels satisfying |γ_(i) ^((l))| < γ_(th) on G_(T) while  keepc_(1b) to get G_(s)  6:  T ← computational cost of G_(S)  7:  if T >T_(b) then  8:   γ_(lo) ← γ_(th)  9:  else 10:   γ_(hi) ← γ_(th) 11: end if 12: end while

In operation 808, the image compression system 130 trains the second GANusing similarity-based knowledge distillation from the first GAN. Theimage compression system 130 transfers knowledge between the twonetworks' feature spaces. Specifically, to avoid information loss (dueto the uneven number of channels between the first GAN and the secondGAN), the image compression system 130 encourages similarity between thetwo feature spaces directly. To compare the similarity between the firstGAN and the second GAN, the image compression system 130 computes asimilarity metric. The similarity metric discussed herein will bereferred to ask the Global-CKA (GCKA). The GCKA is defined as follows:

GCKA(X,Y)=CKA(ρ(X),ρ(Y)),

where X, and Y are the same two tensors and where ρ:

^(n×hwc)→

^(nhw×c) is a reshape operation on the input matrix. The GCKA sumsfeature similarity over channels, characterizing both batch-wise andspatial-wise similarity. The computational complexity of this operationis lower than conventional similarity metrics. The image compressionsystem 130 conducts distillation on the features space. For example, ifS_(KD) denotes the set of layers for performing knowledge distillation,whereas X¹ _(t) and X¹ _(s) denote feature tensors of layer 1 from thefirst GAN and second GAN respectively. The image compression system 130minimizes the distillation loss L_(dist) as follows:

${\mathcal{L}_{dist} = {- {\sum\limits_{l \in S_{KD}}{{GCKA}\left( {X_{t}^{(l)},X_{s}^{(l)}} \right)}}}},$

where the minus sign is introduced to maximize feature similaritybetween the first and second GANs. The first GAN is trained usingoriginal loss functions, which includes an adversarial loss L_(adv) asfollows:

_(adv)=

_(x,y)[log D(x,y)]+

_(x)[log(1−D(x,G(x)))],

where x and y denote the input and real images and D and G denote thediscriminator and generator respectively. For training the second GAN,the image compression system 130 may use the data generated from thefirst GAN to perform the paired data and train the second GAN the sameas the first GAN with a reconstruction loss L_(recon). The second GANmay thus be a compressed for of the first GAN. In some examples, theoverall loss for the second GAN may be described as follows:

_(T)=λ_(adv)

_(adv)+λ_(recon)

_(recon)+λ_(dist)

_(dist).

In some examples, the overall loss for the second GAN may be describedas follows:

_(T)=λ_(adv)

_(adv)+λ_(recon)

_(recon)+λ_(fm)

_(fm)+λ_(dist)

_(dist).

where λ_(adv), λ_(recon), λ_(dist) and λ_(fm) indicate hyper-parametersthat balance the losses.

In operation 810, method 800 stores the trained second GAN. In someexamples the trained second GAN may be deployed on a mobile clientdevice.

Class-Adaptive Normalization with Average Smoothing (CLADE-Avg)

Image quality of compressed images by synthesis with semanticinformation can be improved significantly via introducingspatial-dependency on input to the learned scaling and shift parametersof the normalization layers (named SPADE, a.k.a. GauGAN). The method canbe performed by the image compression system 130 in FIG. 1 .

As shown in FIG. 8B, the input is utilized to determine the learnablescaling and shifting parameters for batch normalization. According tothis disclosure, the generated image quality during image compressioncan be significantly improved, in comparison with the native method ofusing learnable parameters without leveraging the input imageinformation.

For example, for a sample dataset including cityscapes, the generativemodel based on a SPADE module (GauGAN) can achieve a meanIntersection-over-Union (mIoU) of 62.3 (the larger the better), whilethe original work of a Pix2Pix model only achieves an mIoU of 42.06.Even for more advanced techniques like Pix2PixHD, the SPADE module alsoresults in improved performance. For example, on a sample cityscapesdataset, the mIoU with PixPixHD is only 14.6, and the Fréchet inceptiondistance (FID) is as large as 111.5 (the smaller the better), while themIoU with GauGAN can be 37.4, which is more than two times larger, andthe FID is only 22.6, which is around five times smaller. The FID is ametric used to assess the quality of images generated by the generator604 of the GAN.

Albeit its success and popularity, SPADE introduces a huge computationalcost. The computational cost for GauGAN on a sample cityscapes datasetis around 281B MACs, while the original Pix2Pix model only requires56.8B MACs, which results in around a four times difference, andpotentially prohibiting wide application of GauGAN in practice. Learnedparameters are not changing very much along the spatial dimension ineach uniform area, but the learned parameters are only sensitive on theclasses of input pixels. Based on this, the spatial dependency can bereplaced with class-dependent parameters (CLADE), reducing thecomputational cost by a large extent. However, in comparison with theoriginal SPADE method, CLADE saves computational efforts at the cost ofinferior image fidelity.

According to this disclosure, a novel variant is introduced into CLADE,named CLADE-Avg, which recovers the image quality without introducingextra computational cost. This method achieves comparable or even betterperformance than SPADE, and thus this method integrates the advantagesof both existing methods.

As shown in FIG. 8C, the CLADE method uses an input class, instead ofinput pixel information, to determine the scaling and shifted parametersin the following normalization layer. The basic reasoning is that thelearned parameters do not change much across the uniform region for eachinput, but only change across the boundaries where the input semanticinformation changes. Based on this, the input is replaced with the classinformation, reducing the computational cost. However, this willintroduce abrupt changing in the learned parameters, which is differentfrom the SPADE method where these parameters change gradually on theboundaries.

As shown in FIG. 8D, to overcome this issue, this disclosure introducesan extra layer of average smoothing 820 between the parameter layers 822and the normalization layers 824. Compared with CLADE, this methodsmooths the abrupt boundaries, and introduces more possible values forthe scaling and shift. In addition, the kernel size for the averagesmoothing 820 can be selected as a hyperparameter. Here, a 3×3 kernel isused for the purpose of demonstration. This method does not introduceextra multiplications, but only addition, and thus this method does notintroduce much computational overhead, as the division can be absorbedinto the parameters after training, and is referred to as CLADE-Avg.

To compare this CLADE-Avg method with previous methods, a samplecityscape dataset is processed with SPADE, CLADE, and CLADE-Avg, andreport the FID together with computational cost (FLOPs). The results aresummarized in Table 1. As can be seen, this CLADE-Avg method achievesthe best performance efficiency tradeoffs among the three methods.

TABLE 1 Model MACs FID GauGAN  281 B 55.15 Clade 75.2 B 55.82 Clade-Avg75.2 B 54.52

Referring to FIG. 8E there is shown an example method 830 for generatinga compressed image-to-image model on an image 602 using Clade-Avg methodin view of FIG. 8D.

The method 830 is performed by the image compression system 130 in FIG.1 . In one example, a processor (or circuitry dedicated to performinginstructed tasks) included in the image compression system 130 performsthe method 830 or causes the image component to perform the method 830.

At block 832, the image compression system 130 of the GAN receives imageinput for generating a compressed image-to-image model, where in imageinput has parameters that are learned by the GAN. The learned parameterscan include spatial dependency.

At block 834, the GAN uses the input class of the image input, ratherthan pixel data, to determine scaling and shift parameters in thenormalization layers 824. The learned parameters do not change muchacross the uniform region for each input, but only change across theboundaries where the input semantic information changes. Based on this,the input is replaced with the class information, reducing thecomputational cost. However, this will introduce abrupt changing in thelearned parameters, which is different from the previous method thatthese parameters change gradually on the boundaries.

At block 836, the kernel size of the inception-based residual block 702is selected. As previously discussed, the inception-based residual block702 incorporates depth-wise blocks (blocks 704, 706, 708) (e.g.,depth-wise convolutional layers). Depth-wise convolutional layersrequire less computation cost without sacrificing performance and aresuitable for neural networks that are deployed on mobile computingdevices.

At block 838, the GAN performs average smoothing 820 between theparameter layers 822 and the normalization layers 824, as illustrated inFIG. 8D. Compared with CLADE, this method smooths the abrupt boundaries,and introduces more possible values for the scaling and shift. Inaddition, the kernel size for the average smoothing can be selected as ahyperparameter. In one example, a 3×3 kernal can be used.

Machine Architecture

FIG. 9 is a diagrammatic representation of the machine 900 within whichinstructions 910 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 900 to performany one or more of the methodologies discussed herein may be executed.For example, the instructions 910 may cause the machine 900 to executeany one or more of the methods described herein. The instructions 910transform the general, non-programmed machine 900 into a particularmachine 900 programmed to carry out the described and illustratedfunctions in the manner described. The machine 900 may operate as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 900 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 900 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smartphone, a mobile device, a wearable device(e.g., a smartwatch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 910, sequentially or otherwise, that specify actions to betaken by the machine 900. Further, while only a single machine 900 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 910 to perform any one or more of the methodologiesdiscussed herein. The machine 900, for example, may comprise the clientdevice 102 or any one of a number of server devices forming part of themessaging server system 108. In some examples, the machine 900 may alsocomprise both client and server systems, with certain operations of aparticular method or algorithm being performed on the server-side andwith certain operations of the particular method or algorithm beingperformed on the client-side.

The machine 900 may include processors 904, memory 906, and input/outputI/O components 902, which may be configured to communicate with eachother via a bus 940. In an example, the processors 904 (e.g., a CentralProcessing Unit (CPU), a Reduced Instruction Set Computing (RISC)Processor, a Complex Instruction Set Computing (CISC) Processor, aGraphics Processing Unit (GPU), a Digital Signal Processor (DSP), anApplication Specific Integrated Circuit (ASIC), a Radio-FrequencyIntegrated Circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 908 and aprocessor 912 that execute the instructions 910. The term “processor” isintended to include multi-core processors that may comprise two or moreindependent processors (sometimes referred to as “cores”) that mayexecute instructions contemporaneously. Although FIG. 9 shows multipleprocessors 904, the machine 900 may include a single processor with asingle-core, a single processor with multiple cores (e.g., a multi-coreprocessor), multiple processors with a single core, multiple processorswith multiples cores, or any combination thereof.

The memory 906 includes a main memory 914, a static memory 916, and astorage unit 918, both accessible to the processors 904 via the bus 940.The main memory 906, the static memory 916, and storage unit 918 storethe instructions 910 for any one or more of the methodologies orfunctions described herein. The instructions 910 may also reside,completely or partially, within the main memory 914, within the staticmemory 916, within machine-readable medium 920 within the storage unit918, within at least one of the processors 904 (e.g., within theProcessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 900.

The I/O components 902 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 902 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 902 mayinclude many other components that are not shown in FIG. 9 . In variousexamples, the I/O components 902 may include user output components 926and user input components 928. The user output components 926 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 928 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 902 may include biometriccomponents 930, motion components 932, environmental components 934, orposition components 936, among a wide array of other components. Forexample, the biometric components 930 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 932 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 934 include, for example, one or cameras(with still image/photograph and video capabilities), illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 360° camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera and a depth sensor, for example.

The position components 936 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 902 further include communication components 938operable to couple the machine 900 to a network 922 or devices 924 viarespective coupling or connections. For example, the communicationcomponents 938 may include a network interface Component or anothersuitable device to interface with the network 922. In further examples,the communication components 938 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), WiFi® components, and othercommunication components to provide communication via other modalities.The devices 924 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 938 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 938 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components938, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 914, static memory 916, andmemory of the processors 904) and storage unit 918 may store one or moresets of instructions and data structures (e.g., software) embodying orused by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 910), when executedby processors 904, cause various operations to implement the disclosedexamples.

The instructions 910 may be transmitted or received over the network922, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components938) and using any one of several well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions 910 maybe transmitted or received using a transmission medium via a coupling(e.g., a peer-to-peer coupling) to the devices 924.

Software Architecture

FIG. 10 is a block diagram 1000 illustrating a software architecture1004, which can be installed on any one or more of the devices describedherein. The software architecture 1004 is supported by hardware such asa machine 1002 that includes processors 1020, memory 1026, and I/Ocomponents 1038. In this example, the software architecture 1004 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1004 includes layerssuch as an operating system 1012, libraries 1010, frameworks 1008, andapplications 1006. Operationally, the applications 1006 invoke API calls1050 through the software stack and receive messages 1052 in response tothe API calls 1050.

The operating system 1012 manages hardware resources and provides commonservices. The operating system 1012 includes, for example, a kernel1014, services 1016, and drivers 1022. The kernel 1014 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1014 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1016 canprovide other common services for the other software layers. The drivers1022 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1022 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., USB drivers), WI-FI®drivers, audio drivers, power management drivers, and so forth.

The libraries 1010 provide a common low-level infrastructure used by theapplications 1006. The libraries 1010 can include system libraries 1018(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1010 can include APIlibraries 1024 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 1010 can also include a widevariety of other libraries 1028 to provide many other APIs to theapplications 1006.

The frameworks 1008 provide a common high-level infrastructure that isused by the applications 1006. For example, the frameworks 1008 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 1008 canprovide a broad spectrum of other APIs that can be used by theapplications 1006, some of which may be specific to a particularoperating system or platform.

In an example, the applications 1006 may include a home application1036, a contacts application 1030, a browser application 1032, a bookreader application 1034, a location application 1042, a mediaapplication 1044, a messaging application 1046, a game application 1048,and a broad assortment of other applications such as a third-partyapplication 1040. The applications 1006 are programs that executefunctions defined in the programs. Various programming languages can beemployed to generate one or more of the applications 1006, structured ina variety of manners, such as object-oriented programming languages(e.g., Objective-C, Java, or C++) or procedural programming languages(e.g., C or assembly language). In a specific example, the third-partyapplication 1040 (e.g., an application developed using the ANDROID™ orIOS™ software development kit (SDK) by an entity other than the vendorof the particular platform) may be mobile software running on a mobileoperating system such as IOS™, ANDROID™, WINDOWS® Phone, or anothermobile operating system. In this example, the third-party application1040 can invoke the API calls 1050 provided by the operating system 1012to facilitate functionality described herein.

Glossary

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In variousexamples, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors) may be configured by software (e.g., an applicationor application portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a field-programmable gate array (FPGA) or an applicationspecific integrated circuit (ASIC). A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering examples in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In examples in which multiple hardware componentsare configured or instantiated at different times, communicationsbetween such hardware components may be achieved, for example, throughthe storage and retrieval of information in memory structures to whichthe multiple hardware components have access. For example, one hardwarecomponent may perform an operation and store the output of thatoperation in a memory device to which it is communicatively coupled. Afurther hardware component may then, at a later time, access the memorydevice to retrieve and process the stored output. Hardware componentsmay also initiate communications with input or output devices, and canoperate on a resource (e.g., a collection of information). The variousoperations of example methods described herein may be performed, atleast partially, by one or more processors that are temporarilyconfigured (e.g., by software) or permanently configured to perform therelevant operations. Whether temporarily or permanently configured, suchprocessors may constitute processor-implemented components that operateto perform one or more operations or functions described herein. As usedherein, “processor-implemented component” refers to a hardware componentimplemented using one or more processors. Similarly, the methodsdescribed herein may be at least partially processor-implemented, with aparticular processor or processors being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors 904 or processor-implemented components.Moreover, the one or more processors may also operate to supportperformance of the relevant operations in a “cloud computing”environment or as a “software as a service” (SaaS). For example, atleast some of the operations may be performed by a group of computers(as examples of machines including processors), with these operationsbeing accessible via a network (e.g., the Internet) and via one or moreappropriate interfaces (e.g., an API). The performance of certain of theoperations may be distributed among the processors, not only residingwithin a single machine, but deployed across a number of machines. Insome examples, the processors or processor-implemented components may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In otherexamples, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo and the like. The access time for the ephemeral message may be setby the message sender. Alternatively, the access time may be a defaultsetting or a setting specified by the recipient. Regardless of thesetting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

What is claimed is:
 1. A method of operating a generative adversarialnetwork (GAN), comprising: receiving an image having learned parameters;using an input class of the image to determine scaling and shiftingparameters in a normalization layer; and compressing the image using thedetermined scaling and shifting parameters to smooth abrupt boundarieswhere semantic information changes.
 2. The method of claim 1, whereinthe learned parameters include spatial dependency.
 3. The method ofclaim 1, wherein the method further comprises performing averagesmoothing between parameter layers and normalization layers, wherein theaverage smoothing generates a plurality of values for the scaling andshifting parameters.
 4. The method of claim 1, further comprising usingan inception-based residual block containing a kernel.
 5. The method ofclaim 4, wherein the kernel has a kernel size selected from differentkernel sizes.
 6. The method of claim 4, wherein the inception-basedblock incorporates depth-wise convolutional layers.
 7. The method ofclaim 1, wherein the GAN is stored on a mobile computing device.
 8. Themethod of claim 1, wherein the GAN is a pre-trained GAN.
 9. A systemcomprising: a processor; and a memory storing computer readableinstructions that, when executed by the processor, configure the systemto perform operations comprising: receiving an image having learnedparameters; using an input class of the image to determine scaling andshifting parameters in a normalization layer; and compressing the imageusing the determined scaling and shifting parameters to smooth abruptboundaries where semantic information changes.
 10. The system of claim9, wherein the learned parameters include spatial dependency.
 11. Thesystem of claim 9, wherein the computer readable instructions areconfigured to perform average smoothing, wherein the average smoothinggenerates a plurality of values for the scaling and shifting parameters.12. The system of claim 9, further comprising using an inception-basedresidual block containing a kernel.
 13. The system of claim 12, whereinthe kernel has a kernel size selected from different kernel sizes. 14.The system of claim 12, wherein the inception-based block incorporatesdepth-wise convolutional layers.
 15. The system of claim 9, wherein theoperations are performed using a generative adversarial network (GAN)stored on a mobile computing device.
 16. The system of claim 15, whereinthe GAN is a pre-trained GAN.
 17. A non-transitory computer-readablestorage medium including instructions that, when executed by a computer,cause the computer to perform operations comprising: receiving an imagehaving learned parameters; using an input class of the image todetermine scaling and shifting parameters in a normalization layer; andcompressing the image using the determined scaling and shiftingparameters to smooth abrupt boundaries where semantic informationchanges.
 18. The non-transitory computer-readable storage medium ofclaim 17, wherein the learned parameters include spatial dependency. 19.The non-transitory computer-readable storage medium of claim 17, whereinthe instructions are configured to perform average smoothing, whereinthe average smoothing generates a plurality of values for the scalingand shifting parameters.
 20. The non-transitory computer-readablestorage medium of claim 17, further comprising instructions to use aninception-based residual block containing a kernel.